Toward Resilient Electric Vehicle Charging Monitoring Systems: Curriculum Guided Multi-Feature Fusion Transformer

Abstract

With the booming adoption of Electric Vehicles (EVs) globally, the need for reliable and resilient EV Charging Monitoring (EVCM) systems has become crucial. A major challenge in real-time EVCM is the handling of missing data caused by unexpected events, which can impair both real-time monitoring and its downstream applications. To address this vital yet underexplored issue, we propose a curriculum guided multi-feature fusion transformer (CurriFusFormer) learning framework – a novel approach designed to enhance the resilience of EVCM systems against real-time information omissions. Our framework integrates curriculum learning with a multi-feature fusion transformer model, capable of handling various patterns and rates of missing data, ranging from random to block omissions. This innovative approach leverages spatial, temporal, and static features to generate accurate real-time estimations for missing values in diverse scenarios. Extensive experiments on a real-world EVCM dataset demonstrate that CurriFusFormer can perform well with $R^{2}$ ranging from 0.92 to 0.83 given the rising missing rate from 30-90%, outperforming seven popular and state-of-the-art methods, especially in scenarios with high missing rates and complex patterns, such as, at 90% missing rate, kNN ( $R^{2} =0.65$ ), XGBoost ( $R^{2} =0.78$ ), BRITS ( $R^{2} =0.79$ ), TFT ( $R^{2} =0.80$ ), and GRIN ( $R^{2} =0.82$ ). All results suggest that the proposed framework could be a promising solution for developing future resilient EVCM networks.